The invention relates generally to locating the position of a mobile radio communication unit and, more particularly, to radio signal time of arrival measurements.
The ability to locate the position of a mobile radio communication unit provides many well known advantages. Exemplary uses of such position locating capability include security applications, emergency response applications, and travel guidance applications. Conventional techniques for providing position locating capability include time of arrival (TOA) and time difference of arrival (TDOA) techniques.
Referring to FIG. 1, if a radio receiving station 11 can determine the time that a radio signal, transmitted at a predetermined time by a radio transmitting station 13, arrives at the receiving station 11, this arrival time can be used in conventional TOA and TDOA applications. Because the time of transmission is known, the arrival time can be determined, for example, by determining the propagation time associated with the radio communication channel 15 between the two stations. This propagation time can then be multiplied by the speed of light to produce an estimate of the geometric distance between the two stations. If a plurality of fixed-site receiving stations measure the respective times of arrival of a signal transmitted by a mobile transmitting station, or if a mobile receiving station measures the times of arrival of a plurality of signals respectively transmitted by a plurality of fixed-site transmitting stations, then the respective distances from the mobile station. to plural fixed-site stations can be determined and used in conventional fashion to estimate the location of the mobile station.
As an example, an uplink time of arrival measurement approach will now be described with respect to the Global System for Mobile Communication (GSM), which is exemplary of a wireless communication system in which uplink time of arrival techniques are applicable. When an external application (or the GSM network itself) decides to locate the position of a mobile unit (also referred to as mobile station), a Mobile Location Center forces (via a base station controller) the mobile unit to perform a conventional asynchronous handover, whereupon the mobile unit transmits up to 70 uplink access bursts, one burst per TDMA frame (i.e., one burst every 8 time slots). The mobile unit transmits the access bursts in an attempt to comply with the asynchronous handover command.
The Mobile Location Center (MLC) orders a number of Location Measurement Units (LMUs) to capture the access bursts and measure the time of arrival of each burst at each LMU. The LMUs then provide the MLC with their time of arrival measurements and reliability estimates for these measurements. In order to compute the position of the mobile station, the MLC uses the time of arrival values and corresponding reliability parameters, the geographic location coordinates of the LMUs, and information regarding time differences among the respective internal time bases of the LMUs. For example, each LMU can be provided with an absolute time reference (e.g., a Global Positioning System (GPS) clock), in which case the LMUs are all synchronized together, so that relative time differences among the LMUs are not a factor in the MLC""s calculation of the position of the mobile station.
Normally, the bursts contain two parts, one part that is a known sequence, often referred to as a training sequence, and one part that contains data that is unknown to the recipient. Noise, interference and multipath propagation are the main problems when estimating the TOA of a set of bursts. In the case when the signal-to-noise-and-interference ratio (SNIR) is high, and multipath propagation consequently is the main difficulty, a variety of techniques exist to address the TOA estimation problem. The opposite case is when the SNIR is very low. In this case, the effects of multipath propagation are often neglected and all efforts are concentrated on xe2x80x9cfindingxe2x80x9d the bursts, i.e., estimating TOA with an accuracy in the order of 0.5-1 symbol interval. This is sometimes referred to as training sequence detection or burst synchronization.
It is desirable to provide for TOA estimation that can operate both under high and low SNIR. The present invention particularly addresses the TOA estimation problem under low SNIR, i.e., the detection problem.
Consider I bursts transmitted on a channel, each burst containing the same finite sequence s(t) of known bits (e.g., a training sequence), together with other bits that are not known to the receiver. The bursts are delayed between transmitter and receiver by a propagation time xcex94 which, as mentioned above, is the goal to determine. Denote by xi(t) the received signal for a given burst i, where t is (continuous) time. All functions described herein will be in continuous time for simplicity. Since the considered signals normally are bandlimited, analogous digital processing of sampled values can be done instead according to the Nyquist theorem, as will be apparent to workers in the art.
If time dispersion is neglected the received signal can be modeled as
xi(t)=xcex1is(txe2x88x92xcex94)+mi(t)xe2x80x83xe2x80x83Equation 1
where xcex1i is the received signal amplitude of burst i, which amplitude is varying because the channel is fading. The term mi(t) is the sum of noise and interference for burst i. In a cellular system the interference comes from users in other cells transmitting on the same frequency. The noise power E[|mi(t)|2] is normally varying strongly between the bursts, so the noise is nonstationary. This can occur, for example, because the interferer signal is fading or because of frequency hopping in the system. Within a burst however, the noise is often considered as white and stationary.
The state of the art algorithm for estimating xcex94 is called incoherent integration (ICI), described for example in U.S. Ser. No. 08/978,960 filed on Nov. 26, 1997, hereby incorporated herein by reference. Essentially, the algorithm works as follows. Define
ci(xcex94)=∫s(txe2x88x92xcex94)xi*(t)dtxe2x80x83xe2x80x83Equation 2
which is the correlation result between the received signal xi(t) associated with burst i and the known sequence s(t). If SNIR is low, Ci(xcex94) has multiple peaks which are shown in the graph of |Ci(xcex94)|2 in FIG. 2. Compute                               g          ⁡                      (            Δ            )                          =                              ∑                          i              =              1                        I                    ⁢                                    "LeftBracketingBar"                                                c                  i                                ⁡                                  (                  Δ                  )                                            "RightBracketingBar"                        2                                              Equation        ⁢                  xe2x80x83                ⁢        3            
and pick the xcex94* (i.e., the value of xcex94) that maximizes g(xcex94). FIGS. 3 and 4 show examples of g(xcex94) for I=10 and I=50, respectively. ICI performs relatively poorly under interference (nonstationary noise) conditions.
A variation of ICI is weighted ICI which works as follows. Let                                           g            w                    ⁡                      (            Δ            )                          =                              ∑                          i              =              1                        I                    ⁢                                    w              i                        ⁢                                          "LeftBracketingBar"                                                      c                    i                                    ⁡                                      (                    Δ                    )                                                  "RightBracketingBar"                            2                                                          Equation        ⁢                  xe2x80x83                ⁢        4            
and pick the xcex94* that maximizes gw(xcex94). The wi are weight factors designed, for example, to amplify bursts having high SNIR and suppress bursts having low SNIR. This makes the peaks more visible than with Equation 3, as shown by comparing FIGS. 3 and 4 to FIGS. 5 (I=10) and 6 (I=50). The calculation of the weight factors is quite complicated. The optimal weight factors depend on the SNIR for the associated bursts, but the SNIR cannot be estimated until xcex94* is known (or has been estimated). Thus, when using Equation 4, xcex94* is needed to estimate xcex94*. One way to address this problem is to make an a priori estimate of xcex94*, and use it to determine the weight factors wi. However, such an a priori estimate can often disadvantageously deviate several symbol intervals from the correct value. Moreover, weighted ICI also requires collecting and storing all of the received signals xi(t) before evaluation of Equation 4 above, which is a disadvantageous restriction in many applications, for example, in mobile radio receivers where storage capacity is typically limited.
Although weighted ICI performs acceptably under nonstationary noise (interference) conditions, it performs poorly in stationary white noise.
It is desirable in view of the foregoing to provide for burst detection with improved performance in both stationary and nonstationary noise, and which does not require significant data storage capacity. The present invention provides such burst detection capability by implementing an accumulated logarithm function, based on the correlation between the transmitted and received signals and also on the energies of those signals, to estimate the burst propagation delay.